class_docstr: A class for converting a dataframe and/or url to a vectorstore embedded with a given embeddings model
class_docstr_tok: ["A","class","for","converting","a","dataframe","and\/or","url","to","a","vectorstore","embedded","with","a","given","embeddings","model"]
docstr: create batch of document embeddings
docstr_tok: ["create","batch","of","document","embeddings"]
code: def _create_batch_embeddings(
      documents: List[Document], embeddings_batch_size
  ):
      """
      create batch of document embeddings
      """
  
      for i in range(0, len(documents), embeddings_batch_size):
          yield documents[i : i + embeddings_batch_size]
  
file_path: mindsdb/mindsdb/integrations/handlers/rag_handler/ingest.py
imports: import time
  from typing import List
  import pandas
  from langchain.schema import Document
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain.vectorstores import VectorStore
  from mindsdb.integrations.handlers.rag_handler.settings import PersistedVectorStoreSaver, PersistedVectorStoreSaverConfig, RAGBaseParameters, VectorStoreFactory, df_to_documents, get_chroma_client, load_embeddings_model, url_to_documents
  from mindsdb.utilities import log
label_desc: Use image node_id 5 for calling the RAGIngestor obj's underlying member method code with example usage: obj._create_batch_embeddings(documents, embeddings_batch_size) without return types